The role of magnetic resonance imaging biomarkers in clinical trials of treatment response in cancer

A machine learning model based on preoperative multiparametric quantitative DWI can effectively predict the survival and recurrence risk of pancreatic ductal adenocarcinoma

PURPOSE

To develop a machine learning (ML) model combining preoperative multiparametric diffusion-weighted imaging (DWI) and clinical features to better predict overall survival (OS) and recurrence-free survival (RFS) following radical surgery for pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

A retrospective analysis was conducted on 234 PDAC patients who underwent radical resection at two centers. Among 101 ML models tested for predicting postoperative OS and RFS, the best-performing model was identified based on comprehensive evaluation metrics, including C-index, Brier scores, AUC curves, clinical decision curves, and calibration curves. This model's risk stratification capability was further validated using Kaplan-Meier survival analysis.

RESULTS

The random survival forest model achieved the highest C-index (0.828/0.723 for OS and 0.781/0.747 for RFS in training/validation cohorts). Incorporating nine key factors-D value, T-stage, ADC-value, postoperative 7th day CA19-9 level, AJCC stage, tumor differentiation, type of operation, tumor location, and age-optimized the model's predictive accuracy. The model had integrated Brier score below 0.13 and C/D AUC values above 0.85 for both OS and RFS predictions. It also outperformed traditional models in predictive ability and clinical benefit, as shown by clinical decision curves. Calibration curves confirmed good predictive consistency. Using cut-off scores of 16.73/29.05 for OS/RFS, Kaplan-Meier analysis revealed significant prognostic differences between risk groups (p < 0.0001), highlighting the model's robust risk prediction and stratification capabilities.

CONCLUSION

The random survival forest model, combining DWI and clinical features, accurately predicts survival and recurrence risk after radical resection of PDAC and effectively stratifies risk to guide clinical treatment.

CRITICAL RELEVANCE STATEMENT

The construction of 101 ML models based on multiparametric quantitative DWI combined with clinical variables has enhanced the prediction performance for survival and recurrence risks in patients undergoing radical resection for PDAC.

KEY POINTS

This study first develops DWI-based radiological-clinical ML models predicting PDAC prognosis. Among 101 models, RFS is the best and outperforms other traditional models. Multiparametric DWI is the key prognostic predictor, with model interpretations through SurvSHAP.

The value of intravoxel incoherent motion model-based diffusion-weighted imaging for predicting long-term outcomes in nasopharyngeal carcinoma

Objective

The aim of this study was to evaluate the prognostic value for survival of parameters derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in patients with nasopharyngeal carcinoma (NPC).

Materials

Baseline IVIM-DWI was performed on 97 newly diagnosed NPC patients in this prospective study. The relationships between the pretreatment IVIM-DWI parametric values (apparent diffusion coefficient (ADC), D, D*, and f) of the primary tumors and the patients’ 3-year survival were analyzed in 97 NPC patients who received chemoradiotherapy. The cutoff values of IVIM parameters for local relapse-free survival (LRFS) were identified by a non-parametric log-rank test. The local-regional relapse-free survival (LRRFS), LRFS, regional relapse-free survival (RRFS), distant metastasis-free survival (DMFS), progression-free survival (PFS), and overall survival (OS) rates were calculated by using the Kaplan–Meier method. A Cox proportional hazards model was used to explore the independent predictors for prognosis.

Results

There were 97 participants (mean age, 48.4 ± 10.5 years; 65 men) analyzed. Non-parametric log-rank test results showed that the optimal cutoff values of ADC, D, D*, and f were 0.897 × 10−3 mm2/s, 0.699 × 10−3 mm2/s, 8.71 × 10−3 mm2/s, and 0.198%, respectively. According to the univariable analysis, the higher ADC group demonstrated significantly higher OS rates than the low ADC group (p = 0.036), the higher D group showed significantly higher LRFS and OS rates than the low D group (p = 0.028 and p = 0.017, respectively), and the higher D* group exhibited significantly higher LRFS and OS rates than the lower D* group (p = 0.001 and p = 0.002, respectively). Multivariable analyses indicated that ADC and D were the independent prognostic factors for LRFS (p = 0.041 and p = 0.037, respectively), D was an independent prognostic factor for LRRFS (p = 0.045), D* and f were the independent prognostic factors for OS (p = 0.019 and 0.029, respectively), and f acted was an independent prognostic factor for DMFS (p = 0.020).

Conclusions

Baseline IVIM-DWI perfusion parameters ADC and D, together with diffusion parameter D*, could act as useful factors for predicting long-term outcomes and selecting high-risk patients with NPC.

Preoperative Multiparametric Quantitative Magnetic Resonance Imaging Correlates with Prognosis and Recurrence Patterns in Pancreatic Ductal Adenocarcinoma

Simple Summary

Magnetic resonance imaging (MRI) has been considered a noninvasive prognostic biomarker in some cancers; however, the correlation with pancreatic ductal adenocarcinoma (PDAC) remains inconclusive. The aim of our study was to identify quantitative MRI parameters associated with prognosis and recurrence patterns. In an analysis of data from the 136 patients ultimately included in this study, we found that the value of the pure diffusion coefficient D in intravoxel incoherent MRI is an independent risk factor for overall survival (OS) and recurrence-free survival (RFS), while a low value of D is significantly associated with a higher risk of local recurrence. All the patients have been operated on with histopathology for further evaluation. Based on the results of our research, we believe that it is possible in clinical practice to stratify patients based on quantitative MRI data in order to guide treatment strategies, reduce the risk of local tumor recurrence, and improve patients’ prognosis.

Abstract

Magnetic resonance imaging (MRI) has been shown to be associated with prognosis in some tumors; however, the correlation in pancreatic ductal adenocarcinoma (PDAC) remains inconclusive. In this retrospective study, we ultimately included 136 patients and analyzed quantitative MRI parameters that are associated with prognosis and recurrence patterns in PDAC using survival analysis and competing risks models; all the patients have been operated on with histopathology and immunohistochemical staining for further evaluation. In intravoxel incoherent motion diffusion-weighted imaging (DWI), we found that pure-diffusion coefficient D value was an independent risk factor for overall survival (OS) (HR: 1.696, 95% CI: 1.003–2.869, p = 0.049) and recurrence-free survival (RFS) (HR: 2.066, 95% CI: 1.252–3.409, p = 0.005). A low D value (≤1.08 × 10⁻³ mm²/s) was significantly associated with a higher risk of local recurrence (SHR: 5.905, 95% CI: 2.107–16.458, p = 0.001). Subgroup analysis revealed that patients with high D and f values had significantly better outcomes with adjuvant chemotherapy. Distant recurrence patients in the high-D value group who received chemotherapy may significantly improve their OS and RFS. It was found that preoperative multiparametric quantitative MRI correlates with prognosis and recurrence patterns in PDAC. Diffusion coefficient D value can be used as a noninvasive biomarker for predicting prognosis and recurrence patterns in PDAC.

Cross-Modality Imaging of Murine Tumor Vasculature-a Feasibility Study

Tumor vasculature and angiogenesis play a crucial role in tumor progression. Their visualization is therefore of utmost importance to the community. In this proof-of-principle study, we have established a novel cross-modality imaging (CMI) pipeline to characterize exactly the same murine tumors across scales and penetration depths, using orthotopic models of melanoma cancer. This allowed the acquisition of a comprehensive set of vascular parameters for a single tumor. The workflow visualizes capillaries at different length scales, puts them into the context of the overall tumor vessel network and allows quantification and comparison of vessel densities and morphologies by different modalities. The workflow adds information about hypoxia and blood flow rates. The CMI approach includes well-established technologies such as magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and ultrasound (US), and modalities that are recent entrants into preclinical discovery such as optical coherence tomography (OCT) and high-resolution episcopic microscopy (HREM). This novel CMI platform establishes the feasibility of combining these technologies using an extensive image processing pipeline. Despite the challenges pertaining to the integration of microscopic and macroscopic data across spatial resolutions, we also established an open-source pipeline for the semi-automated co-registration of the diverse multiscale datasets, which enables truly correlative vascular imaging. Although focused on tumor vasculature, our CMI platform can be used to tackle a multitude of research questions in cancer biology.

Cross-Modality Imaging of Murine Tumor Vasculature-a Feasibility Study

Tumor vasculature and angiogenesis play a crucial role in tumor progression. Their visualization is therefore of utmost importance to the community. In this proof-of-principle study, we have established a novel cross-modality imaging (CMI) pipeline to characterize exactly the same murine tumors across scales and penetration depths, using orthotopic models of melanoma cancer. This allowed the acquisition of a comprehensive set of vascular parameters for a single tumor. The workflow visualizes capillaries at different length scales, puts them into the context of the overall tumor vessel network and allows quantification and comparison of vessel densities and morphologies by different modalities. The workflow adds information about hypoxia and blood flow rates. The CMI approach includes well-established technologies such as magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and ultrasound (US), and modalities that are recent entrants into preclinical discovery such as optical coherence tomography (OCT) and high-resolution episcopic microscopy (HREM). This novel CMI platform establishes the feasibility of combining these technologies using an extensive image processing pipeline. Despite the challenges pertaining to the integration of microscopic and macroscopic data across spatial resolutions, we also established an open-source pipeline for the semi-automated co-registration of the diverse multiscale datasets, which enables truly correlative vascular imaging. Although focused on tumor vasculature, our CMI platform can be used to tackle a multitude of research questions in cancer biology.

Intravoxel incoherent motion diffusion-weighted imaging assessment of microvascular characteristics in the murine embryonal rhabdomyosarcoma model

Background

Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) can distinguish the false diffusion generated by microvascular blood flow from the true water molecule diffusion.

Purpose

To investigate the correlation between IVIM-DWI parameters and angiogenic markers such as the microvessel density and vascular endothelial growth factor (VEGF) expression in the murine embryonal rhabdomyosarcoma model.

Material and Methods

The murine embryonal rhabdomyosarcoma model was produced by subcutaneously injecting 107 human embryonal rhabdomyosarcoma cells into the right back of nude mice. The apparent diffusion coefficient (ADC), pseudo-diffusion coefficient (D*), true diffusion coefficient (D), and perfusion fraction (f) were obtained from 22 mice models using IVIM-DWI with b-values of 0, 50, 100, 150, 200, 400, 600, 800, 1000, and 1200 s/mm2. The microvessel density and VEGF expression were obtained by histologic examination. We then compared the correlation between IVIM-DWI parameters and microvessel density and VEGF expression.

Results

The average ADC, D*, D, and f values were 1.05 ± 0.27 × 10−3 mm2/s, 6.19 ± 1.78 × 10−3 mm2/s, 0.69 ± 0.09 ×10−3 mm2/s, and 17.68 ± 8.41 (%), respectively. There was moderate positive correlation between D* value and microvessel density and VEGF expression (r = 0.484, P = 0.023; r = 0.511, P = 0.015). However, there was no significant correlation between ADC, D, and f values and microvessel density and VEGF expression.

Conclusion

The D* value from IVIM-DWI may be used to evaluate tumor angiogenesis in the murine embryonal rhabdomyosarcoma model.

Measurement of Tumor Pressure and Strategies of Imaging Tumor Pressure for Radioimmunotherapy

Tumor interstitial pressure is a fundamental feature of cancer biology. Elevation in tumor pressure affects the efficacy of cancer treatment and results in the heterogenous intratumoral distribution of drugs and macromolecules. Monoclonal antibodies (mAb) play a prominent role in cancer therapy and molecular nuclear imaging. Therapy using mAb labeled with radionuclides—also known as radioimmunotherapy (RIT)—is an effective form of cancer treatment. RIT is clinically effective for the treatment of lymphoma and other blood cancers; however, its clinical use for solid tumor was limited because their high interstitial pressure prevents mAb from penetrating into the tumor. This pressure can be decreased using anti-cancer drugs or additional external therapy. In this paper, we reviewed the intratumoral pressure using direct tumor-pressure measurement strategies, such as the wick-in-needle and pressure catheter transducer method, and indirect tumor-pressure measurement strategies via magnetic resonance.

The added value of whole-body magnetic resonance imaging in the management of patients with advanced breast cancer

This study investigates the impact of whole-body MRI (WB-MRI) in addition to CT of chest-abdomen-pelvis (CT-CAP) and 18F-FDG PET/CT (PET/CT) on systemic treatment decisions in standard clinical practice for patients with advanced breast cancer (ABC). WB-MRI examinations in ABC patients were extracted from our WB-MRI registry (2009-2017). Patients under systemic treatment who underwent WB-MRI and a control examination (CT-CAP or PET/CT) were included. Data regarding progressive disease (PD) reported either on WB-MRI or on the control examinations were collected. Data regarding eventual change in treatment after the imaging evaluation were collected. It was finally evaluated whether the detection of PD by any of the two modalities had induced a change in treatment. Among 910 WB-MRI examinations in ABC patients, 58 had a paired control examination (16 CT-CAP and 42 PET/CT) and were analysed. In 23/58 paired examinations, additional sites of disease were reported only on WB-MRI and not on the control examination. In 17/28 paired examinations, PD was reported only on WB-MRI and not on the control examination. In 14 out of the 28 pairs of examinations that were followed by a change in treatment, PD had been reported only on WBMRI (14/28; 50%), while stable disease had been reported on the control examination. In conclusion, WB-MRI disclosed PD earlier than the control examination (CT-CAP or PET/CT), and it was responsible alone for 50% of all changes in treatment.

Diffusion-Weighted MRI Is Insensitive to Changes in the Tumor Microenvironment Induced by Antiangiogenic Therapy

Antiangiogenic treatment (AAT) used in combination with radiation therapy or chemotherapy is a promising strategy for the treatment of several cancer diseases. The vascularity and oxygenation of tumors may be changed significantly by AAT, and consequently, a noninvasive method for monitoring AAT-induced changes in these microenvironmental parameters is needed. The purpose of this study was to evaluate the potential usefulness of diffusion-weighted magnetic resonance imaging (DW-MRI). DW-MRI was conducted with a Bruker Biospec 7.05-T scanner using four diffusion weightings and diffusion sensitization gradients in three orthogonal directions. Maps of the apparent diffusion coefficient (ADC) were calculated by using a monoexponential diffusion model. Two cervical carcinoma xenograft models (BK-12, HL-16) were treated with bevacizumab, and two pancreatic carcinoma xenograft models (BxPC-3, Panc-1) were treated with sunitinib. Pimonidazole and CD31 were used as markers of hypoxia and blood vessels, respectively, and fraction of hypoxic tissue (HF_Pim) and microvascular density (MVD) were quantified by analyzing immunohistochemical preparations. MVD decreased significantly after AAT in BK-12, HL-16, and BxPC-3 tumors, and this decrease was sufficiently large to cause a significant increase in HF_Pim in BK-12 and BxPC-3 tumors. The ADC maps of treated tumors and untreated control tumors were not significantly different in any of these three tumor models, suggesting that the AAT-induced microenvironmental changes were not detectable by DW-MRI. DW-MRI is insensitive to changes in tumor vascularity and oxygenation induced by bevacizumab or sunitinib treatment.

The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains

PURPOSE

Quantitative evaluation of dynamic contrast enhanced MRI (DCE-MRI) allows for estimating perfusion, vessel permeability, and tissue volume fractions by fitting signal intensity curves to pharmacokinetic models. These compart mental models assume rapid equilibration of contrast agent within each voxel. However, there is increasing evidence that this assumption is violated for small molecular weight gadolinium chelates. To evaluate the error introduced by this invalid assumption, we simulated DCE-MRI experiments with volume fractions computed from entire histological tumor cross-sections obtained from murine studies.

METHODS

A 2D finite element model of a diffusion-compensated Tofts-Kety model was developed to simulate dynamic T₁ signal intensity data. Digitized histology slices were segmented into vascular (v_p ), cellular and extravascular extracellular (v_e ) volume fractions. Within this domain, K^trans (the volume transfer constant) was assigned values from 0 to 0.5 min^-1 . A representative signal enhancement curve was then calculated for each imaging voxel and the resulting simulated DCE-MRI data analyzed by the extended Tofts-Kety model.

RESULTS

Results indicated parameterization errors of -19.1% ± 10.6% in K^trans , -4.92% ± 3.86% in v_e , and 79.5% ± 16.8% in v_p for use of Gd-DTPA over 4 tumor domains.

CONCLUSION

These results indicate a need for revising the standard model of DCE-MRI to incorporate a correction for slow diffusion of contrast agent. Magn Reson Med 80:330-340, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy.

Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results

Pathologic complete response following neoadjuvant therapy (NAT) is used as a short-term surrogate marker of eventual outcome in patients with breast cancer. Analyzing voxel-level heterogeneity in MRI-derived parametric maps, obtained before and after the first cycle of NAT ([Formula: see text]), in conjunction with receptor status, may improve the predictive accuracy of tumor response to NAT. Toward that end, we incorporated two MRI-derived parameters, the apparent diffusion coefficient and efflux rate constant, with receptor status in a logistic ridge-regression model. The area under the curve (AUC) and Brier score of the model computed via 10-fold cross validation were 0.94 (95% CI: 0.85, 0.99) and 0.11 (95% CI: 0.06, 0.16), respectively. These two statistics strongly support the hypothesis that our proposed model outperforms the other models that we investigated (namely, models without either receptor information or voxel-level information). The contribution of the receptor information was manifested by an 8% to 15% increase in AUC and a 14% to 21% decrease in Brier score. These data indicate that combining multiparametric MRI with hormone receptor status has a high likelihood of improved prediction of pathologic response to NAT in breast cancer.

To predict the radiosensitivity of nasopharyngeal carcinoma using intravoxel incoherent motion MRI at 3.0 T

PURPOSE

To investigate intravoxel incoherent motion (IVIM) MRI for evaluating the sensitivity of radiotherapy on nasopharyngeal carcinoma (NPC).

RESULTS

The reproducibility between intra-observer and inter-observer was relatively good. D (0.72×10^-3 mm²/s±0.14 vs. 0.54×10^-3 mm²/s±0.23; P < 0.001) and D* (157.92×10^-3 mm²/s±15.21 vs. 120.36×10^-3 mm²/s±10.22; P < 0.0001) were significantly higher in effective group than poor-effective group, whereas the difference of f (18.79%±2.51 vs. 16.47%±1.51) and ADC (1.21×10^-3 mm²/s±0.11 vs. 1.33×10^-3 mm²/s±0.23) could not reach statistical significant between the 2 groups (P > 0.05).

CONCLUSIONS

IVIM may be potentially useful in assessing the radiosensitivity of NPC. The higher D value combining with higher D* value might indicate the more radiosensitive of NPC, and increased D* might reflect increased blood vessel generation and parenchymal perfusion in NPC.

MATERIALS AND METHODS

Sixty consecutive patients (20 female, range, 27-83 years, mean age, 52 years) newly diagnosed NPC in the stage of T3 or T4 were enrolled. Forty-two of them were divided into effective group clinically after a standard radiotherapy according to the RECIST criteria. IVIM with 13 b-values (range, 0-800 s/mm²) and general MRI were performed at 3.0T MR scanner before and after radiotherapy. The parameters of IVIM including perfusion fraction (f), perfusion-related diffusion (D*), pure molecular diffusion (D) and apparent diffusion coefficient (ADC) were calculated. Two radiologists major in MRI diagnose analyzed all images independently and placed regions of interest (ROIs). Intra-class correlation coefficient (ICC) was used to evaluate intra-observer and inter-observer agreement. And Mann-Whitney test was used to assess the differences between the two groups.

Use of non-invasive imaging to monitor response to aflibercept treatment in murine models of colorectal cancer liver metastases

The liver is the most frequent metastatic site in colorectal cancer (CRC), and relevant orthotopic in vivo models are needed to study the efficacy of anticancer drugs in the metastatic setting. A challenge when utilizing such models is monitoring tumor growth during the experiments. In this study, experimental liver metastases were established in nude mice by splenic injection of the CRC cell lines HT29 and HCT116, and the mice were treated with the antiangiogenic drug aflibercept. Tumor growth was monitored using magnetic resonance imaging (MRI) and bioluminescence imaging (BLI). Aflibercept treatment was well tolerated and resulted in increased animal survival in HCT116, but not in HT29, while inhibited tumor growth was observed in both models. Treatment efficacy was monitored with high precision using MRI, while BLI detected small-volume disease with high sensitivity, but was less accurate in end-stage disease. Apparent diffusion coefficient (ADC) values obtained by diffusion weighted MRI (DW-MRI) were highly predictive of treatment response, with increased ADC corresponding well with areas of necrosis observed by histological evaluation of aflibercept-treated xenografts. The results showed that the efficacy of the antiangiogenic drug aflibercept varied between the two models, possibly reflecting unique growth patterns in the liver that may be representative of human disease. Non-invasive imaging, especially MRI and DW-MRI, can be used to effectively monitor tumor growth and treatment response in orthotopic liver metastasis models.

Diffusion-weighted and dynamic contrast-enhanced MRI of pancreatic adenocarcinoma xenografts: associations with tumor differentiation and collagen content

PURPOSE

The aggressiveness of pancreatic ductal adenocarcinoma (PDAC) is highly dependent on the level of differentiation and the composition of the stroma. In this preclinical study, we investigated the potential of diffusion-weighted magnetic resonance imaging (DW-MRI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as noninvasive methods for providing information on the differentiation and the stroma of PDACs.

METHODS

Xenografted tumors initiated from four PDAC cell lines (BxPC-3, Capan-2, MIAPaCa-2, and Panc-1) were included in the study. DW-MRI and DCE-MRI were carried out on a 7.05-T MR scanner, and tumor images of ADC (the apparent diffusion coefficient), K (trans) (the volume transfer constant of Gd-DOTA), and v e (the fractional distribution volume of Gd-DOTA) were produced. The level of differentiation and the amount and structure of collagen I and collagen IV were determined by examining histological preparations.

RESULTS

Differentiated tumors showed lower levels of collagen I and collagen IV than non-differentiated tumors. Significant correlations were found between ADC and v e, and both parameters differentiated clearly between collagen-rich non-differentiated tumors and differentiated tumors containing less collagen.

CONCLUSION

Differentiated PDAC xenografts show higher ADC values and higher v e values than their non-differentiated counterparts. This observation supports the application of parametric MR images as tumor biomarkers in PDAC. Patients showing low values of ADC and v e most likely have non-differentiated tumors with extensive stroma and, hence, poor prognosis.

Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer

OBJECTIVES

The purpose of this study was to determine whether multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI), obtained before and after the first cycle of neoadjuvant chemotherapy (NAC), is superior to single-parameter measurements for predicting pathologic complete response (pCR) in patients with breast cancer.

MATERIALS AND METHODS

Patients with stage II/III breast cancer were enrolled in an institutional review board-approved study in which 3-T DCE-MRI and DWI data were acquired before (n = 42) and after 1 cycle (n = 36) of NAC. Estimates of the volume transfer rate (K), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = K/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety model. The apparent diffusion coefficient (ADC) was estimated from the DWI data. The derived parameter kep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC.

RESULTS

The kep/ADC after the first cycle of NAC discriminated patients who went on to achieve a pCR (P < 0.001) and achieved a sensitivity, specificity, positive predictive value, and area under the receiver operator curve (AUC) of 0.92, 0.78, 0.69, and 0.88, respectively. These values were superior to the single parameters kep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between kep/ADC and kep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018-0.23), whereas the AUCs between kep/ADC and ADC trended toward significance (-0.11 to 0.24).

CONCLUSIONS

The multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.

Early effects of low dose bevacizumab treatment assessed by magnetic resonance imaging

Background

Antiangiogenic treatments have been shown to increase blood perfusion and oxygenation in some experimental tumors, and to reduce blood perfusion and induce hypoxia in others. The purpose of this preclinical study was to investigate the potential of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted MRI (DW-MRI) in assessing early effects of low dose bevacizumab treatment, and to investigate intratumor heterogeneity in this effect.

Methods

A-07 and R-18 human melanoma xenografts, showing high and low expression of VEGF-A, respectively, were used as tumor models. Untreated and bevacizumab-treated tumors were subjected to DCE-MRI and DW-MRI before treatment, and twice during a 7-days treatment period. Tumor images of K^trans (the volume transfer constant of Gd-DOTA) and v_e (the fractional distribution volume of Gd-DOTA) were produced by pharmacokinetic analysis of the DCE-MRI data, and tumor images of ADC (the apparent diffusion coefficient) were produced from DW-MRI data.

Results

Untreated A-07 tumors showed higher K^trans, v_e, and ADC values than untreated R-18 tumors. Untreated tumors showed radial heterogeneity in K^trans, i.e., K^trans was low in central tumor regions and increased gradually towards the tumor periphery. After the treatment, bevacizumab-treated A-07 tumors showed lower K^trans values than untreated A-07 tumors. Peripherial tumor regions showed substantial reductions in K^trans, whereas little or no effect was seen in central regions. Consequently, the treatment altered the radial heterogeneity in K^trans. In R-18 tumors, significant changes in K^trans were not observed. Treatment induced changes in tumor size, v_e, and ADC were not seen in any of the tumor lines.

Conclusions

Early effects of low dose bevacizumab treatment may be highly heterogeneous within tumors and can be detected with DCE-MRI.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1918-1) contains supplementary material, which is available to authorized users.

Magnetic Resonance Imaging, Digital Mammography, and Sonography: Tumor Characteristics and Tumor Biology in Primary Setting

The use of imaging in the arena of primary treatment for breast cancer is gaining importance as a technique for assessing response to chemotherapy as well as assessing the underlying tumor biology. Both mammography and ultrasound have traditionally been used, in addition to clinical evaluation, to evaluate response to treatment although they have shed little light on the underlying biological processes. Functional magnetic resonance imaging techniques have the ability to assess response to treatments in addition to providing valuable information on changes in tumor perfusion, vascular permeability, oxygenation, cellularity, proliferation, and metabolism both at baseline and after treatment. This noninvasive method of evaluating cellular function is of importance both as endpoints for clinical trials and to our understanding of the biological mechanisms of cancer.

Assessing response to treatment of bone metastases from breast cancer: what should be the standard of care?

Bone is the most common site for breast cancer metastases, occurring in up to 70% of those with metastatic disease. In order to effectively manage these patients, it is essential to have consistent, reproducible and validated methods of assessing response to therapy. We present current clinical practice of imaging response assessment of bone metastases. We also review the biology of bone metastases and measures of response assessment including clinical assessment, tumour markers and imaging techniques; bone scans (BSs), computed tomography (CT), positron emission tomography, magnetic resonance imaging (MRI) and whole-body diffusion-weighted MRI (WB DW-MRI). The current standard of care of BSs and CT has significant limitations and are not routinely recommended for the purpose of response assessment in the bones. WB DW-MRI has the potential to address this unmet need and should be evaluated in clinical trials.

Surveillance radiologic imaging after treatment of oropharyngeal cancer: a review

The increasing proportion of human papilloma virus-related oropharynx cancers has led to improved success in the treatment of this disease. However, the current low recurrence rate after treatment of oropharyngeal cancer highlights the continued need for, as well as the challenges of, designing an effective follow-up surveillance program. There are frequently multiple modalities used in the treatment of oropharyngeal cancer, resulting in short- and long-term tissue changes to the head and neck that challenge clinical distinction of recurrence versus treatment-related changes. The oropharynx subsite is characterized by complex anatomy not always accessible to physical exam, making radiologic imaging a potentially useful supplement for effective follow-up assessment. In this manuscript, the literature regarding the type of radiologic imaging modality and the frequency of obtaining imaging studies in the surveillance follow-up after treatment of oropharyngeal cancer is reviewed. While ultrasound and MRI have useful characteristics that deserve further study, PET/CT appears to have the best sensitivity and specificity for imaging surveillance follow-up of head and neck cancers including oropharyngeal cancer. A negative PET/CT is particularly useful as a predictor of prognosis and can guide the clinician as to when to stop obtaining additional imaging studies in the absence of clinical signs of recurrence. However, there is scant evidence that imaging surveillance can improve survival outcomes. Suggestions to guide future imaging surveillance research studies are provided.

Quantitative multimodality imaging in cancer research and therapy

Advances in hardware and software have enabled the realization of clinically feasible, quantitative multimodality imaging of tissue pathophysiology. Earlier efforts relating to multimodality imaging of cancer have focused on the integration of anatomical and functional characteristics, such as PET-CT and single-photon emission CT (SPECT-CT), whereas more-recent advances and applications have involved the integration of multiple quantitative, functional measurements (for example, multiple PET tracers, varied MRI contrast mechanisms, and PET-MRI), thereby providing a more-comprehensive characterization of the tumour phenotype. The enormous amount of complementary quantitative data generated by such studies is beginning to offer unique insights into opportunities to optimize care for individual patients. Although important technical optimization and improved biological interpretation of multimodality imaging findings are needed, this approach can already be applied informatively in clinical trials of cancer therapeutics using existing tools. These concepts are discussed herein.

Multi-parametric MRI characterization of inflammation in murine skeletal muscle

Myopathies often display a common set of complex pathologies that include muscle weakness, inflammation, compromised membrane integrity, fat deposition, and fibrosis. Multi-parametric, quantitative, non-invasive imaging approaches may be able to resolve these individual pathological components. The goal of this study was to use multi-parametric MRI to investigate inflammation as an isolated pathological feature. Proton relaxation, diffusion tensor imaging (DTI), quantitative magnetization transfer (qMT-MRI), and dynamic contrast enhanced (DCE-MRI) parameters were calculated from data acquired in a single imaging session conducted 6-8 hours following the injection of λ-carrageenan, a local inflammatory agent. T2 increased in the inflamed muscle and transitioned to bi-exponential behavior. In diffusion measurements, all three eigenvalues and the apparent diffusion coefficient increased, but λ3 had the largest relative change. Analysis of the qMT data revealed that the T1 of the free pool and the observed T1 both increased in the inflamed tissue, while the ratio of exchanging spins in the solid pool to those in the free water pool (the pool size ratio) significantly decreased. DCE-MRI data also supported observations of an increase in extracellular volume. These findings enriched the understanding of the relation between multiple quantitative MRI parameters and an isolated inflammatory pathology, and may potentially be employed for other single or complex myopathy models.

DW-MRI in assessment of the hypoxic fraction, interstitial fluid pressure, and metastatic propensity of melanoma xenografts

Background

Cancer patients with primary tumors showing extensive hypoxia and highly elevated interstitial fluid pressure (IFP) have poor prognosis. The potential of diffusion-weighted magnetic resonance imaging (DW-MRI) in assessing the hypoxic fraction, IFP, and metastatic propensity of tumors was investigated in this study.

Methods

A-07 and R-18 melanoma xenografts were used as general models of human cancer. DW-MRI was performed at 1.5 T, and maps of the apparent diffusion coefficient (ADC) were produced with in-house-made software developed in Matlab. Pimonidazole was used as a hypoxia marker. Tumor cell density and hypoxic fraction were assessed by quantitative analysis of histological sections. IFP was measured with a Millar catheter. Metastatic propensity was determined by examining tumor-bearing mice for pulmonary micrometastases post mortem.

Results

ADC decreased with increasing tumor cell density, independent of whether the A-07 and R-18 data were analyzed separately or together. In the A-07 line, ADC decreased with increasing hypoxic fraction and increasing IFP and was lower in metastatic than in nonmetastatic tumors, and in the R-18 line, ADC decreased with increasing hypoxic fraction. There was a strong inverse correlation between ADC and hypoxic fraction as well as between ADC and IFP across the two tumor lines, primarily because low ADC as well as high hypoxic fraction and high IFP were associated with high cell density.

Conclusion

Low ADC is a potentially useful biomarker of poor prognosis in cancer, since low ADC is mainly a consequence of high cell density, and high cell density may lead to increased hypoxia and interstitial hypertension and, therefore, increased microenvironment-associated metastasis.

Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer

The purpose of this study is to investigate the ability of multivariate analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parametric maps, obtained early in the course of therapy, to predict which patients will achieve pathologic complete response (pCR) at the time of surgery. Thirty-three patients underwent DCE-MRI (to estimate K (trans), v e, k ep, and v p) and DW-MRI [to estimate the apparent diffusion coefficient (ADC)] at baseline (t 1) and after the first cycle of neoadjuvant chemotherapy (t 2). Four analyses were performed and evaluated using receiver-operating characteristic (ROC) analysis to test their ability to predict pCR. First, a region of interest (ROI) level analysis input the mean K (trans), v e, k ep, v p, and ADC into the logistic model. Second, a voxel-based analysis was performed in which a longitudinal registration algorithm aligned serial parameters to a common space for each patient. The voxels with an increase in k ep, K (trans), and v p or a decrease in ADC or v e were then detected and input into the regression model. In the third analysis, both the ROI and voxel level data were included in the regression model. In the fourth analysis, the ROI and voxel level data were combined with selected clinical data in the regression model. The overfitting-corrected area under the ROC curve (AUC) with 95% confidence intervals (CIs) was then calculated to evaluate the performance of the four analyses. The combination of k ep, ADC ROI, and voxel level data achieved the best AUC (95% CI) of 0.87 (0.77-0.98).

Magnetic resonance imaging identifies early effects of sunitinib treatment in human melanoma xenografts

BACKGROUND

Antiangiogenic treatment may change the tumor microenvironment and hence influence the effect of conventional therapies. The potential of diffusion weighted magnetic resonance imaging (DW-MRI) and dynamic contrast enhanced MRI (DCE-MRI) in assessing microenvironmental effects of sunitinib treatment was investigated in this preclinical study.

METHODS

Sunitinib-treated and untreated A-07 tumors were subjected to DW-MRI and DCE-MRI, and parametric images of ADC and Ktrans were produced. Microvascular density, hypoxic fraction, and necrotic fraction were assessed from immunohistochemical preparations, and tumor interstitial fluid pressure (IFP) was assessed with probe measurement.

RESULTS

Sunitinib-treated tumors showed reduced microvascular density, increased hypoxic fraction, increased necrotic fraction, increased ADC, and reduced Ktrans, but did not differ from untreated tumors in growth rate and IFP.

CONCLUSIONS

Sunitinib treatment affected the tumor microenvironment without affecting tumor size. DW-MRI and DCE-MRI were sensitive to the sunitinib-induced changes in the tumor microenvironment.

The use of tumour volumetrics to assess response to therapy in anticancer clinical trials

Serial evaluations of tumour burden using imaging, mainly computed tomography and magnetic resonance imaging, form the basis for assessing treatment response in many clinical trials of anticancer therapeutics. Traditionally, these evaluations have been based on linear measurements of tumour size. Such measurements have limitations related to variability in technical factors, tumour morphology and reader decisions. Measurements of entire tumour volumes may overcome some of the limitations of linear tumour measurements, improving our ability to detect small changes reliably and increasing statistical power per subject in a trial. Certain technical factors are known to affect the accuracy and precision of volume measurements, and work is in progress to define these factors more thoroughly and to qualify tumour volume as a biomarker for the purposes of drug development.

Quantifying heterogeneity in human tumours using MRI and PET

Most tumours, even those of the same histological type and grade, demonstrate considerable biological heterogeneity. Variations in genomic subtype, growth factor expression and local microenvironmental factors can result in regional variations within individual tumours. For example, localised variations in tumour cell proliferation, cell death, metabolic activity and vascular structure will be accompanied by variations in oxygenation status, pH and drug delivery that may directly affect therapeutic response. Documenting and quantifying regional heterogeneity within the tumour requires histological or imaging techniques. There is increasing evidence that quantitative imaging biomarkers can be used in vivo to provide important, reproducible and repeatable estimates of tumoural heterogeneity. In this article we review the imaging methods available to provide appropriate biomarkers of tumour structure and function. We also discuss the significant technical issues involved in the quantitative estimation of heterogeneity and the range of descriptive metrics that can be derived. Finally, we have reviewed the existing clinical evidence that heterogeneity metrics provide additional useful information in drug discovery and development and in clinical practice.

Early prediction of the response of breast tumors to neoadjuvant chemotherapy using quantitative MRI and machine learning

The ability to predict early in the course of treatment the response of breast tumors to neoadjuvant chemotherapy can stratify patients based on response for patient-specific treatment strategies. Currently response to neoadjuvant chemotherapy is evaluated based on physical exam or breast imaging (mammogram, ultrasound or conventional breast MRI). There is a poor correlation among these measurements and with the actual tumor size when measured by the pathologist during definitive surgery. We tested the feasibility of using quantitative MRI as a tool for early prediction of tumor response. Between 2007 and 2010 twenty consecutive patients diagnosed with Stage II/III breast cancer and receiving neoadjuvant chemotherapy were enrolled on a prospective imaging study. Our study showed that quantitative MRI parameters along with routine clinical measures can predict responders from non-responders to neoadjuvant chemotherapy. The best predictive model had an accuracy of 0.9, a positive predictive value of 0.91 and an AUC of 0.96.

Development of a New Tool for 3D Modeling for Regenerative Medicine

The effectiveness of therapeutic treatment based on regenerative medicine for degenerative diseases (i.e., neurodegenerative or cardiac diseases) requires tools allowing the visualization and analysis of the three-dimensional (3D) distribution of target drugs within the tissue. Here, we present a new computational procedure able to overcome the limitations of visual analysis emerging by the examination of a molecular signal within images of serial tissue/organ sections by using the conventional techniques. Together with the 3D anatomical reconstitution of the tissue/organ, our framework allows the detection of signals of different origins (e.g., marked generic molecules, colorimetric, or fluorimetric substrates for enzymes; microRNA; recombinant protein). Remarkably, the application does not require the employment of specific tracking reagents for the imaging analysis. We report two different representative applications: the first shows the reconstruction of a 3D model of mouse brain with the analysis of the distribution of theβ-Galactosidase, the second shows the reconstruction of a 3D mouse heart with the measurement of the cardiac volume.